





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


A_		:  Tuple		 =						logging.get_logger(__name__)



def        UpperCamelCase__		(						__magic_name__  :   str		,       __magic_name__  :   int      )				->			int:
 '''simple docstring'''






 snake_case__     :					List[Any]    =  b.T
 snake_case__     :					Union[str, Any]    =  np.sum(np.square(__magic_name__      )		,       axis=1      )
 snake_case__     :					List[Any]    =  np.sum(np.square(__magic_name__      )		,       axis=0      )
 snake_case__     :					Dict    =  np.matmul(__magic_name__		,       __magic_name__      )
 snake_case__     :					List[str]    =  aa[:, None] - 2 * ab + ba[None, :]
 return d



def        UpperCamelCase__		(						__magic_name__  :   Union[str, Any]		,       __magic_name__  :   Any      )				->			Union[str, Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  x.reshape(-1		,       3      )
 snake_case__     :					Optional[int]    =  squared_euclidean_distance(__magic_name__		,       __magic_name__      )
 return np.argmin(__magic_name__		,       axis=1      )





class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      ['''pixel_values''']

   def __init__(							self	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = True	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,		__SCREAMING_SNAKE_CASE = True	,		__SCREAMING_SNAKE_CASE = True	,		**__SCREAMING_SNAKE_CASE	,		):
    super().__init__(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  size if size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
    snake_case__     :					Optional[Any]    =  get_size_dict(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[Any]    =  np.array(__SCREAMING_SNAKE_CASE       ) if clusters is not None else None
    snake_case__     :					Tuple    =  do_resize
    snake_case__     :					int    =  size
    snake_case__     :					int    =  resample
    snake_case__     :					List[Any]    =  do_normalize
    snake_case__     :					Any    =  do_color_quantize

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,		__SCREAMING_SNAKE_CASE = None	,		**__SCREAMING_SNAKE_CASE	,		):
    snake_case__     :					Any    =  get_size_dict(__SCREAMING_SNAKE_CASE       )
    if "height" not in size or "width" not in size:
     raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"       )
    return resize(
        __SCREAMING_SNAKE_CASE	,		size=(size["""height"""], size["""width"""])	,		resample=__SCREAMING_SNAKE_CASE	,		data_format=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = None	,		):
    snake_case__     :					Any    =  rescale(image=__SCREAMING_SNAKE_CASE	,		scale=1 / 127.5	,		data_format=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  image - 1
    return image


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,		**__SCREAMING_SNAKE_CASE	,		):
    snake_case__     :					str    =  do_resize if do_resize is not None else self.do_resize
    snake_case__     :					List[str]    =  size if size is not None else self.size
    snake_case__     :					int    =  get_size_dict(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  resample if resample is not None else self.resample
    snake_case__     :					str    =  do_normalize if do_normalize is not None else self.do_normalize
    snake_case__     :					Optional[Any]    =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    snake_case__     :					Any    =  clusters if clusters is not None else self.clusters
    snake_case__     :					List[Any]    =  np.array(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Union[str, Any]    =  make_list_of_images(__SCREAMING_SNAKE_CASE       )

    if not valid_images(__SCREAMING_SNAKE_CASE       ):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""       )

    if do_resize and size is None or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True."""       )

    if do_color_quantize and clusters is None:
     raise ValueError("""Clusters must be specified if do_color_quantize is True."""       )

    # All transformations expect numpy arrays.
    snake_case__     :					Union[str, Any]    =  [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

    if do_resize:
     snake_case__     :					Union[str, Any]    =  [self.resize(image=__SCREAMING_SNAKE_CASE	,		size=__SCREAMING_SNAKE_CASE	,		resample=__SCREAMING_SNAKE_CASE       ) for image in images]

    if do_normalize:
     snake_case__     :					List[Any]    =  [self.normalize(image=__SCREAMING_SNAKE_CASE       ) for image in images]

    if do_color_quantize:
     snake_case__     :					int    =  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,		ChannelDimension.LAST       ) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     snake_case__     :					Optional[Any]    =  np.array(__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Tuple    =  color_quantize(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ).reshape(images.shape[:-1]       )

     # flatten to (batch_size, height*width)
     snake_case__     :					List[Any]    =  images.shape[0]
     snake_case__     :					List[str]    =  images.reshape(__SCREAMING_SNAKE_CASE	,		-1       )

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     snake_case__     :					Optional[int]    =  list(__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					Dict    =  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ) for image in images]

    snake_case__     :					Optional[int]    =  {"""input_ids""": images}
    return BatchFeature(data=__SCREAMING_SNAKE_CASE	,		tensor_type=__SCREAMING_SNAKE_CASE       )





def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->int:




  '''simple docstring'''

  if index == number_of_items:
    return 0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    knapsack(a__    ,     a__    ,     a__    ,     a__    ,     index + 1 )
  if weights[index] <= max_weight:
    _UpperCamelCase           =    values[index] + knapsack(
        a__    ,     a__    ,     a__    ,     max_weight - weights[index]    ,     index + 1 )
  return max(a__    ,     a__ )


if __name__ == "__main__":
   import doctest

   doctest.testmod()




